Title :
Motor Inverter Fault Diagnosis Using Wavelets Neural Networks
Author :
Sheng Qiang ; Yingying Li
Author_Institution :
Dept. of Control Sci. & Eng., Harbin Inst. of Technol., Harbin, China
Abstract :
Diagnosis and location of the power transistor open switch and short circuit switch faults in inverter are studied. Wavelet transform is used to extract diagnostic indices from the current, speed and torque waveforms of brushless DC drive system. RBF neural network is developed to identify and locate the fault. RBF neural networks are trained offline using simulation results under various healthy and faulty conditions from a simulation model. Simulation results confirm the effectiveness of the proposed methodology.
Keywords :
brushless DC motors; fault diagnosis; invertors; power engineering computing; power transistors; radial basis function networks; waveform analysis; wavelet transforms; RBF neural network; brushless DC drive system; current waveforms; motor inverter fault diagnosis; power transistor open switch; short circuit switch; speed waveforms; torque waveforms; wavelet transform; wavelets neural networks; Brushless DC motors; Circuit faults; Fault diagnosis; Inverters; Neural networks; Switching circuits; Wavelet transforms; Inverter Fault diagnosis; Open switch; RBF neural network; Short circuit switch; Wavelet transform;
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on
Conference_Location :
Manchester
DOI :
10.1109/SMC.2013.540
